# Primavera P6 – Monte Carlo Simulation

Do you know that the number of parallel activities or concurrent activities leading to a successor activity (or activities) will have a huge impact on the Start and Finish Dates of the successor activity (or activities)?

I use Full Monte for MSP for all my examples in this article.

We use triangular distribution for Activity A which have a static duration of 10 days.

Using Full Monte software to run Monte Carlo analysis (a topic on Monte Carlo analysis will be our future posting), I then sample Activity A having the following using Triangular Distribution:

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Next we run the schedule with 10,000 (Yes, that is Ten Thousand iterations).

We will then obtain the following cumulative plot. It seems that the average completion date for Activity A is still 12th December 2019 with about a 51% chance of completing on this date.

However, what happens if we have two activities in parallel and are both linked FS to the completion milestones. For simplicity, we have input Activity B as having a similar start and finish dates as Activity A and both have a duration of 10 days.

I also set Activity B having the same triangular distribution as Activity A:

- Optimistic duration 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations, we obtain the following cumulative plot. We now only have a 25% chance of having 12th December 2019 as the completion date. Compare this with only one activity, Activity A above.

Next, we input Activity C into the schedule with Activity C having a similar start and finish dates as Activity A and B, with the following Optimistic, Most Likely and Pessimistic Duration values.

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations with three concurrent activities, we obtain the following cumulative plot. We now only have a 12% chance of having 12th December 2019 as the completion date.

Next, we input Activity D into the schedule with Activity D having a similar start and finish dates as Activity A, B and C.

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations with four concurrent activities, we obtain the following cumulative plot. We now only have a 6% chance of having 12th December 2019 as the completion date.

Next, we input Activity E into the schedule with Activity E having a similar start and finish dates as Activity A, B, C and D.

Using Full Monte to run Monte Carlo simulations with four concurrent activities, we obtain the following cumulative plot. We now only have a 3% chance of having 12th December 2019 as the completion date.

## Summary

The effect of merge bias and concurrent activities can be determined by running Monte Carlo (MC) simulations on schedules. Traditional Critical Path Method (CPM) lacks this ability. Therefore, for large size schedules (this is subjective as we can see in the example above, 3 to 4 activities running concurrently will display merge bias effect – however the more concurrent activities, the more pronounced the merge bias effect will be) with many concurrent or near concurrent activities with many merge points, we would highly recommend our Client on the viability of using MC simulations to determine the chances of meeting the key milestone dates.

# Primavera P6 – Monte Carlo Simulation

Do you know that the number of parallel activities or concurrent activities leading to a successor activity (or activities) will have a huge impact on the Start and Finish Dates of the successor activity (or activities)?

I use Full Monte for MSP for all my examples in this article.

We use triangular distribution for Activity A which have a static duration of 10 days.

Using Full Monte software to run Monte Carlo analysis (a topic on Monte Carlo analysis will be our future posting), I then sample Activity A having the following using Triangular Distribution:

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Next we run the schedule with 10,000 (Yes, that is Ten Thousand iterations).

We will then obtain the following cumulative plot. It seems that the average completion date for Activity A is still 12th December 2019 with about a 51% chance of completing on this date.

However, what happens if we have two activities in parallel and are both linked FS to the completion milestones. For simplicity, we have input Activity B as having a similar start and finish dates as Activity A and both have a duration of 10 days.

I also set Activity B having the same triangular distribution as Activity A:

- Optimistic duration 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations, we obtain the following cumulative plot. We now only have a 25% chance of having 12th December 2019 as the completion date. Compare this with only one activity, Activity A above.

Next, we input Activity C into the schedule with Activity C having a similar start and finish dates as Activity A and B, with the following Optimistic, Most Likely and Pessimistic Duration values.

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations with three concurrent activities, we obtain the following cumulative plot. We now only have a 12% chance of having 12th December 2019 as the completion date.

Next, we input Activity D into the schedule with Activity D having a similar start and finish dates as Activity A, B and C.

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations with four concurrent activities, we obtain the following cumulative plot. We now only have a 6% chance of having 12th December 2019 as the completion date.

Next, we input Activity E into the schedule with Activity E having a similar start and finish dates as Activity A, B, C and D.

Using Full Monte to run Monte Carlo simulations with four concurrent activities, we obtain the following cumulative plot. We now only have a 3% chance of having 12th December 2019 as the completion date.

## Summary

The effect of merge bias and concurrent activities can be determined by running Monte Carlo (MC) simulations on schedules. Traditional Critical Path Method (CPM) lacks this ability. Therefore, for large size schedules (this is subjective as we can see in the example above, 3 to 4 activities running concurrently will display merge bias effect – however the more concurrent activities, the more pronounced the merge bias effect will be) with many concurrent or near concurrent activities with many merge points, we would highly recommend our Client on the viability of using MC simulations to determine the chances of meeting the key milestone dates.

# Primavera P6 – Monte Carlo Simulation

Do you know that the number of parallel activities or concurrent activities leading to a successor activity (or activities) will have a huge impact on the Start and Finish Dates of the successor activity (or activities)?

I use Full Monte for MSP for all my examples in this article.

We use triangular distribution for Activity A which have a static duration of 10 days.

Using Full Monte software to run Monte Carlo analysis (a topic on Monte Carlo analysis will be our future posting), I then sample Activity A having the following using Triangular Distribution:

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Next we run the schedule with 10,000 (Yes, that is Ten Thousand iterations).

We will then obtain the following cumulative plot. It seems that the average completion date for Activity A is still 12th December 2019 with about a 51% chance of completing on this date.

However, what happens if we have two activities in parallel and are both linked FS to the completion milestones. For simplicity, we have input Activity B as having a similar start and finish dates as Activity A and both have a duration of 10 days.

I also set Activity B having the same triangular distribution as Activity A:

- Optimistic duration 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations, we obtain the following cumulative plot. We now only have a 25% chance of having 12th December 2019 as the completion date. Compare this with only one activity, Activity A above.

Next, we input Activity C into the schedule with Activity C having a similar start and finish dates as Activity A and B, with the following Optimistic, Most Likely and Pessimistic Duration values.

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations with three concurrent activities, we obtain the following cumulative plot. We now only have a 12% chance of having 12th December 2019 as the completion date.

Next, we input Activity D into the schedule with Activity D having a similar start and finish dates as Activity A, B and C.

- Optimistic duration of 8 days
- Most Likely duration 10 days
- Pessimistic duration 12 days

Using Full Monte to run Monte Carlo simulations with four concurrent activities, we obtain the following cumulative plot. We now only have a 6% chance of having 12th December 2019 as the completion date.

Next, we input Activity E into the schedule with Activity E having a similar start and finish dates as Activity A, B, C and D.

Using Full Monte to run Monte Carlo simulations with four concurrent activities, we obtain the following cumulative plot. We now only have a 3% chance of having 12th December 2019 as the completion date.

## Summary

The effect of merge bias and concurrent activities can be determined by running Monte Carlo (MC) simulations on schedules. Traditional Critical Path Method (CPM) lacks this ability. Therefore, for large size schedules (this is subjective as we can see in the example above, 3 to 4 activities running concurrently will display merge bias effect – however the more concurrent activities, the more pronounced the merge bias effect will be) with many concurrent or near concurrent activities with many merge points, we would highly recommend our Client on the viability of using MC simulations to determine the chances of meeting the key milestone dates.